Analysis of image motion occurring in a series of successive video frames is traditionally a computationally expensive algorithm, with no practical hardware implementations for real time performance. Recent computer software techniques using hierarchical motion analysis (HMA) algorithms have made the computations significantly more efficient. However, it is still not practical, employing these computer software techniques, to perform cost-effective HMA in real time at the relatively high frame rate (e.g., 30 frames per second) on an ongoing series of successive video images.
Traditional motion analysis, which is performed only on the full 2-dimensional image pixel resolution, requires C*(N.sup.2 *M.sup.2 *K) operations, where C is a constant, N.sup.2 is the image size in pixels at the full 2-dimensional image pixel resolution, M.sup.2 is the maximum expected motion distance in pixels at the full 2-dimensional image pixel resolution, and K is the number of computation operations required for a motion estimate with M equal to 1 pixel. HMA, as now practiced, is performed by computer software algorithms operating on each level of a multiresolution representation of the image in a coarse to fine manner, starting with the lowest resolution. The result of the motion analysis at each level is used as an initial estimate in the next higher resolution level. The motion vectors generated by the motion analysis on the highest resolution level are the final motion vector estimates. The maximum expected motion for the analysis at each level is 1 (.+-.1 pixel), while the total maximum motion can be 2.sup.n, where n+1 is the number of resolution levels of the multiresolution representation. The total required computational operations at all resolution levels, regardless of the number of resolution levels, is now reduced to no more than 4/3*C*(N.sup.2 *K).
An additional advantage of HMA is that local coherent motion is performed more robustly, resulting in a higher confidence in the correctness of the motion estimate.